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import traceback
import streamlit as st
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_openai import ChatOpenAI
from langchain_anthropic import ChatAnthropic
from langchain_google_genai import ChatGoogleGenerativeAI
PROMPT = """
You are an AI assistant specializing in anger management. Your task is to provide thoughtful, practical, and effective advice to help the user manage their anger based on:
- Who the user is angry at: {who}
- The specific situation that caused the anger: {content}
1. Acknowledge the user's feelings to validate their emotions.
2. Provide practical steps or techniques to manage and reduce their anger.
3. Encourage the user to address the underlying issue in a constructive manner.
4. Offer additional resources or tips for similar situations in the future.
"""
def init_page():
st.set_page_config(
page_title="Anger Management - AI Agent Navi",
page_icon="🧘"
)
st.header("Anger Management - AI Agent Navi🧘")
def select_model(temperature=0):
models = ("GPT-4o","GPT-4o-mini", "Claude 3.5 Sonnet", "Gemini 1.5 Pro")
model_choice = st.radio("Choose a model:", models)
if model_choice == "GPT-4o":
return ChatOpenAI(temperature=temperature, model_name="gpt-4o")
elif model_choice == "GPT-4o-mini":
return ChatOpenAI(temperature=temperature, model_name="gpt-4o-mini")
elif model_choice == "Claude 3.5 Sonnet":
return ChatAnthropic(temperature=temperature, model_name="claude-3-5-sonnet-20240620")
elif model_choice == "Gemini 1.5 Pro":
return ChatGoogleGenerativeAI(temperature=temperature, model="gemini-1.5-pro-latest")
def init_chain():
llm = select_model()
prompt = ChatPromptTemplate.from_messages([
("user", PROMPT),
])
output_parser = StrOutputParser()
chain = prompt | llm | output_parser
return chain
def main():
init_page()
# Style adjustments (optional, remove if not needed)
st.markdown("""<style>.st-emotion-cache-15ecox0 { display: none !important; }
@media (max-width: 50.5rem) {.st-emotion-cache-13ln4jf {max-width: calc(0rem + 100vw);}}
</style>""",unsafe_allow_html=True,)
chain = init_chain()
if chain:
who = st.text_input("Who are you angry at?", key="who")
content = st.text_area("What specific situation caused the anger?", key="content")
if st.button("Submit"):
result = chain.stream({"who": who, "content": content})
st.write(result)
if __name__ == '__main__':
main()
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